Beyond Timbral Statistics: Improving Music Classification Using Percussive Patterns and Bass Lines

This paper discusses a new approach for clustering sequences of bar-long percussive and bass-line patterns in audio music collections and its application to genre classification. Many musical genres and styles are characterized by two kinds of distinct representative patterns, i.e., percussive patterns and bass-line patterns. So far, in most automatic genre classification systems, rhythmic and bass melody information has not been effectively used. In order to extract bar-long unit rhythmic patterns for a music collection, we propose a clustering method based on one-pass dynamic programming and k-means clustering. For clustering bass-line patterns, a method based on k -means clustering capable of handling pitch-shifting is proposed. After extracting these two fundamental kinds of patterns for each style/genre, feature vectors which are suitable for representing information about the patterns are proposed for supervised learning. Experimental results show that the automatically calculated rhythmic pattern information and bass pattern information can be used to effectively classify musical genre/style and improve upon current approaches based on timbral features.